What's Next?

Let’s review what we’ve learned so far and what we’ll learn next.

We'll cover the following


Kubernetes relies heavily on available resources spread throughout the cluster. Still, it cannot do magic. We need to help it by defining the resources we expect our containers to consume.

Even though Metrics Server is not the best solution for collecting metrics, it is already available in our k3d cluster. We used it to learn how many resources our applications use, and through that information, we refined our resource definitions.

Without metrics, our definitions are pure guesses. When we guess, Kubernetes needs to guess as well. A stable system is a predictable system based on facts, not random assumptions. Metrics Server helped us transform our assumptions into measurable facts, which we fed into Kubernetes, which, in turn, used them in its scheduling algorithms.

Exploration of resource definitions led us to the Quality of Service (QoS) classes. Even though Kubernetes decides which QoS will be used, knowing the rules used in the decision process is essential if we are to prioritize applications and their availability.

All that leads us to the culmination of the strategies that make our clusters secure, stable, and robust. Dividing a cluster into namespaces and employing RBAC is not enough.

RBAC prevents unauthorized users from accessing the cluster and provides permissions to those we trust. However, RBAC does not prevent users from accidentally (or intentionally) putting the cluster in danger through too many deployments, too big applications, or inaccurate sizing. Only by combining RBAC with resource defaults, limitations, and quotas can we hope for a fault- tolerant and robust cluster capable of reliably hosting our applications.

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